The classic approach for digital image enlargement is to use direct spatial interpolation. This, however, results in image blur, as a result of bilinear interpolation; or image aliasing, as a result of pixel replication.
Resolution enhancement requires that a small image be enlarged to several times its actual size while avoiding blurring, ringing or other artifacts. Classic methods include bilinear or bi-cubic interpolation schemes, followed by an edge sharpening method, such as unsharp masking. Spatial interpolation schemes, however, tend to blur the images when applied indiscriminately. Unsharp masking, which involves subtracting a properly scaled Laplacian of the image from itself, enhances artifacts and image noise. More sophisticated schemes, such as those involving Wavelet or Fractal based techniques, have also been used. Such schemes extrapolate the signal in either the Wavelet or Fractal domain, which leads to objectionable artifacts when the assumptions behind such extrapolation are violated. It may also be noted that such extrapolatory assumptions predict and actively enhance the high frequency content within the image thus increasing any noise present in the sub-sampled image.
I have previously developed an iterative method, which improves the performance of any given base interpolation scheme while not making explicit "high frequency enhancing" assumptions. The main assumption is: interpolation is good until the interpolated data crosses an edge. Instead of making ad hoc extrapolatory assumptions, interpolation is performed in the "right fashion." Other methods have been developed which selectively interpolate across edges. Such methods, however, tend to promote false edges, which lead to noticeable artifacts. This occurs because the location of the edges in the magnified image is itself imprecise because the selectively interpolated across edge technique uses a sub-sampled image, i.e., the given small image and the algorithms make one-step decisions as to the course of action in edge-areas of the image. The iterative nature of the scheme is aimed at avoiding such an error by not committing blindly to a predetermined course of action at edge locations.
High quality image enlargement is needed in desktop imaging applications which demand high quality input and output images. In such applications, classical spatial interpolation methods do not deliver sufficient quality, especially at high enlargement factors, particularly when high-quality displays or printers are used. Blurring or aliasing artifacts become evident as images are enlarged to larger sizes and are viewed or printed on high quality displays or printers. High quality image enlargement may be utilized for high quality printing at different sizes. An enlargement algorithm may be incorporated in a printer. An enlargement algorithm may also be implemented in a scanner to improve the image resolution over the physical resolution capability of the scanner via post-processing, as is commonly done in modern day scanners.